Moisture Dynamics Research Articles

Modeling Temperature and Moisture Dynamics in Corn Storage Silos: A Comparative 2D and 3D Approach

Grain storage in silos plays a fundamental role in preserving the quality and safety of agricultural products. This study presents a comparative evaluation of two-dimensional (2D) and three-dimensional (3D) mathematical models to predict the temperature and moisture distribution during unventilated corn storage in cylindrical silos with conical roofs. The models incorporate external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity via sorption isotherm (water activity), and grain respiration to simulate real-world storage conditions. The 2D model offers computational efficiency and is suitable for preliminary assessments but simplifies natural convection effects and underestimates axial temperature gradients. Conversely, the 3D model provides a detailed representation of heat and moisture transfer phenomena, capturing complex interactions such as buoyancy-driven flow and localized effects of solar radiation. The results reveal that temperature and moisture accumulation are more pronounced in the upper regions of the silo, driven by solar radiation and natural convection, with significant implications for large-scale silos where thermal inertia plays a key role. This dual modeling approach demonstrates that while the 2D model is valuable for quick evaluations, the 3D model is essential for comprehensive insights into thermal and moisture gradients. The findings support informed decision-making in silo design, optimization, and management, enhancing grain storage strategies globally.

Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations

Abstract. The consistency of hydrological models, i.e. their ability to reproduce observed system dynamics, needs to be improved to increase their predictive power. As using streamflow data alone to calibrate models is not sufficient to constrain them and render them consistent, other strategies must be considered, in particular using additional types of data. The aim of this study was to test whether simultaneous calibration of dissolved organic carbon (DOC) and nitrate (NO3-) concentrations along with streamflow improved the hydrological consistency of a parsimonious solute-transport model. A multi-objective approach with four calibration scenarios was used to evaluate the model's predictions for an intensive agricultural headwater catchment. After calibration, the model reasonably simultaneously reproduced the dynamics of discharge and DOC and NO3- concentrations in the stream of the headwater catchment from 2008–2016. Evaluation using independent datasets indicated that the model usually reproduced dynamics of groundwater level and soil moisture in upslope and riparian zones correctly for all calibration scenarios. Using daily stream concentrations of DOC and NO3- along with streamflow to calibrate the model did not improve its ability to predict streamflow for calibration or evaluation periods. The approach significantly improved the representation of groundwater storage and to a lesser extent soil moisture in the upslope zone but not in the riparian zone. Parameter uncertainty decreased when the model was calibrated using solute concentrations, except for parameters related to fast and slow reservoir flow. This study shows the added value of using multiple types of data along with streamflow, in particular DOC and NO3- concentrations, to constrain hydrological models to improve representation of internal hydrological states and flows. With the increasing availability of solute data from catchment monitoring, this approach provides an objective way to improve the consistency of hydrological models that can be used with confidence to evaluate scenarios.

Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes

Abstract Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here we use a 3-D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature estimates by 43.5% in comparison with observational data. Soil moisture has little effect on soil temperature during the wet season (-1.5 %) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and soil temperature, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.

Assessing the impacts of forest cover change on carbon stock and soil moisture dynamics using geospatial techniques: a case study of Nensebo forest, Southern Ethiopia.

Assessing the impacts of forest cover change on carbon stock and soil moisture dynamics is critical for understanding environmental degradation and guiding sustainable land management. This study evaluates the effects of forest cover change on carbon stock and soil moisture dynamics in Nensebo Forest from 1993 to 2023 using geospatial techniques. Landsat imagery including TM (1993), ETM + (2009), and OLI/TIRS (2023) were used. Land use land cover (LULC) classification was performed using supervised classification approach with maximum likelihood algorithm. The study reveals that agricultural land expanded significantly from 143.2 km2 (21.8%) to 230.0 km2 (35.1%), along with increases in bare land and grassland. Moreover, result reveals a substantial decline in vegetation health (forest), with the Normalized Difference Vegetation Index (NDVI) decreasing from 0.78 in 1993 to 0.55 in 2023, indicating anincrease ofdeforestation and land degradation. The soil moisture index (SMI) shows a reduction in soil moisture retention from 0.51 to 0.39, reflecting the adverse impact of forest loss on soil moisture dynamics. Carbon stock analysis reveals a dramatic decrease in forest area from 489.5 to 220.5 km2, leading to increased carbon dioxide emissions from forest loss, which rose from 293.03 tons to 1550.34 tons. The study underscores the urgent need for integrated land management strategies that balance agricultural development with ecological conservation. Therefore, to mitigate climate change and maintain ecological stability, the study recommends implementing sustainable land use practices, promoting reforestation, and developing environmental relatedpolicies to preserve the remaining forest cover while supporting agricultural productivity.

Analysis of Soil Moisture Dynamics and Its Response to Rainfall in the Mu Us Sandy Land

Analysis of Soil Moisture Dynamics and Its Response to Rainfall in the Mu Us Sandy Land

Evolution characteristic of soil water in loess slopes with different slope angles

BackgroundIn the Loess Plateau region, significant engineering activities have led to many exposed loess slopes. These slopes have undergone a series of shallow failures under rainfall, significantly affecting their stability. Vegetation can somewhat restore the ecological damage to the slope surfaces and enhance their stability. Thus, studying the spatiotemporal evolution of soil moisture migration under vegetation protection on loess slopes is crucial.MethodsEmploying experimental designs with slope gradients of 45° and 60°, this investigation is structured around a trio of core objectives: to delineate the processes of rainfall infiltration and its redistribution within the slope, to chart the evolution of soil water within the loess soil matrix, and to discern the impacts of slope inclination on soil water dynamics. Critical to this study are the monitoring of volumetric moisture content, matric suction, and the external variables of rainfall and temperature, alongside an analysis of soil water potential and moisture movement as observed in laboratory setups and simulated through Hydrus-2D.ResultsThe study revealed that slope angle significantly affects soil moisture infiltration and redistribution. The steeper slope (60°) exhibited more pronounced fluctuations in soil water potential, particularly during the rainy season, reflecting the dynamic nature of water movement. This slope also demonstrated sharper transitions in soil moisture during drying periods, indicating a greater sensitivity to weather changes. Water movement parallel to the slope surface was faster on steeper slopes, especially under drying conditions, with more pronounced lateral downslope flow at the surface layer. In contrast, the gentler slope (45°) showed more consistent moisture retention during wet periods, with slower and more uniform soil moisture movement, leading to a steadier moisture gradient and prolonged upslope movement. Vegetation plays a crucial role in modulating soil moisture dynamics, with grass growth being more effective on the steeper 60° slope. The extensive root network on this slope enhanced water retention, increased soil permeability, and reduced erosion. During the drying phase, deeper root systems significantly reduced volumetric water content at shallower depths, promoting higher moisture content in the middle sections of the slope.

Productive moisture dynamics in sod-podzolic soil under spring barley cultivation

The article presents the results of a long-term two-factor stationary experiment in the conditions of the Republic of Mari El, which was conducted in 1996–2021 in two plots. The aim of the research was to study the dynamics of productive moisture in the 0–20 cm layer of sod-podzolic soil depending on the predecessors of spring barley in six-field crop rotations. The predecessors were potatoes with application of 60 t/ha of manure, spring grain crops (oats, spring wheat) and winter cereals (winter wheat and winter rye). In the course of study a correlation and regression data analysis was carried out between the corresponding hydrothermal coefficient of Selyaninov (HTC) and the reserves of available soil water in barley sowings during “shooting” and “wax ripeness” phases after various predecessors. As a result, it was found out that sowing barley after oats led to one unit increase of HTC, that raised the amount of soil water available to plants by 17.45±7.95 mm by the time of barley harvesting. Dead moisture reserves were possible at HTC less than 0.79 in this case. Correlation and regression analysis data for other predecessors were less evident, but retained the noted trend. It was established that the weather conditions of 1996-2021 in the Republic of Mari El to the time of spring barley sowing mainly provided "satisfactory" (20–30 mm) amount of productive soil moisture in the 0–20 cm soil layer. Potatoes were the best predecessors in the experiment. After that during the vegetation of barley the reserves of available water in the soil were least determined by the HTC – R-squared (R2 ) was 0.306±0.096, the amount of productive moisture decreased by 34.6 % on average. After cereal crops, mainly oats, HTC affected significantly the variability of the “amount of productive moisture” indicator by the time of harvesting (R2 up to 0.802±0.028) reducing the yield twice from the moment of sowing (up to 15.1±5.1 mm).

Modeling and Application of the Hydrus-2D Model for Simulating Preferential Flow in Loess Soil Under Various Scenarios

Soil hydraulic properties are mainly governed by the soil’s heterogeneity, anisotropy, and discontinuous structural characteristics, primarily when connected soil macropores characterize the structures. Therefore, researchers must document reliable hydrological models to elucidate how the soil medium affects the movement of soil water. This study, utilizing a field-scale staining tracer test, distinguishes between matrix flow and preferential flow areas in the seepage field of Xi’an loess. The Xi’an loess’s soil water characteristic curve (SWCC) was explored through field investigations and laboratory analyses. A dual-permeability model that couples matrix and macropore flow was developed using the Hydrus-2D model, enabling simulations of water migration under varying initial soil water content, rainfall intensity, and crack width. The results showed that (1) The SWCC of macropores in the preferential flow area exhibits a bimodal distribution, and the Fredlund & Xing model is applied for sectional fitting to obtain the corresponding soil water characteristic parameters. (2) Initial soil water content and rainfall intensity significantly influence water distribution, while crack width has a relatively minor effect. (3) The cumulative flux under the preferential flow is significantly higher than in the matrix area, and the wetting front depth increases with higher initial water content and rainfall intensity. This study reveals the key characteristics of preferential flow and moisture migration in the matrix zone and their influencing factors in loess. It constructs a two-domain infiltration model by integrating loess’s diverse structural characteristics and pore morphology. This model provides a theoretical basis and technical support for simulating preferential flow and studying the moisture dynamics of loess profiles.

Are rootzone soil moisture dynamics and thresholds associated with surface layer?

Abstract The identification of evapotranspiration regimes, primarily the water-limited and energy-limited regimes, separated by the critical soil moisture (CSM) threshold, is fundamental to analyzing land–atmosphere interactions. To better understand the soil moisture (SM) dynamics happening synchronously in the soil column, we aim to estimate the rootzone (0–28 cm and 0–100 cm) CSM thresholds and associated regimes at a global scale, which was not previously attempted. We propose the use of the covariability of soil diurnal temperature amplitude (derived from the GLDAS) and SM (ERA5) to estimate the CSM, which overcomes the data uncertainty and multivariate dependencies of traditional methods. We find that transitional climatic regions, encompassing the western USA, Brazilian savanna, Sahelian grassland, South African savanna, peninsular India, and Mediterranean region, are global hotspots of frequent rootzone regime shifting with significant seasonality—the wet regime prevails in the fall season, while the dry regime takes over at other times of the year. The CSM values of 0–28 cm and 0–100 cm layers are mostly in the 0.2–0.35 and 0.25–0.4 m3m−3 range, respectively. We find that landscape aridity and bioclimatic characteristics primarily determine the spatial distribution of CSM and associated regimes. Furthermore, we investigate the hydrological link between the surface and rootzone layers. We note that the rootzone and surface CSM and regimes are strongly correlated, although the 0–28 cm layer indicates a relatively stronger connection compared to the 0–100 cm layer. The shallower (deeper) rootzone layer shows regimes similar to those on the surface for more than 80% (65%–80%) of the time. We observe that the strength of association between surface and rootzone regimes increases from arid (herbaceous vegetated regions) to humid (woody) regions and during wet to dry seasons. Overall, a strong association in regime dynamics between surface and subsurface layers suggests the potential applicability of remotely sensed surface SM as a surrogate to study rootzone regime responsiveness to soil–plant–atmosphere interactions.

Quantifying the Impacts of Precipitation, Vegetation, and Soil Properties on Soil Moisture Dynamics in Desert Steppe Herbaceous Communities Under Extreme Drought

The security of water resources in the desert steppe ecosystem faces threats due to large-scale vegetation restoration. Dynamic changes in soil moisture result from the interplay of precipitation replenishment and evapotranspiration depletion, both directly regulated by vegetation and soil. To achieve sustainable vegetation restoration, understanding the quantifiable impacts of precipitation, evapotranspiration, soil, and vegetation on spatiotemporal soil moisture dynamics is crucial. However, these effects remain insufficiently understood. In this study, against the background of an extreme drought from 2020 to 2022, four typical herbaceous plant communities—Agropyron mongolicum, Sophora alopecuroides, Stipa breviflora, and Achnatherum splendens—were selected for investigation in Yanchi County, Ningxia Province, Northwest China. We analyzed dynamic changes in soil moisture at 0–120 cm during depletion, recovery, and stability periods, quantifying the relative contributions of precipitation, evapotranspiration, soil clay/sand ratio (C/S), and biomass to soil moisture dynamics. The results showed that the 0–120 cm soil moisture of the four plant communities in the depletion, recovery, and stability periods decreased from 7.38% to 6.81%, 11.22% to 8.08%, and 11.70% to 5.84%, respectively. In terms of relative importance, precipitation and evapotranspiration accounted for 25% to 50% and 23.6% to 39.6% of the total explanation for the soil moisture in each plant community, respectively. C/S primarily influenced soil moisture in the S. alopecuroides community, demonstrating a significant positive correlation with soil moisture and accounting for 49.1% of the total explanation. Biomass mainly affected soil moisture in the A. mongolicum, S. breviflora, and A. splendens communities and had a significant negative correlation with soil moisture, accounting for 5.7%, 13.1%, and 9.8% of the total interpretation, respectively. The continuous extreme drought caused the soil moisture deficit to extend from the shallow to the deep layers. The effects of C/S and biomass on soil moisture occurred in leguminous and gramineous communities, respectively.

Integrating Convolutional Attention and Encoder–Decoder Long Short-Term Memory for Enhanced Soil Moisture Prediction

Soil moisture is recognized as a crucial variable in land–atmosphere interactions. This study introduces the Convolutional Attention Encoder–Decoder Long Short-Term Memory (CAEDLSTM) model to address the uncertainties and limitations inherent in traditional soil moisture prediction methods, especially in capturing complex temporal dynamics across diverse environmental conditions. Unlike existing approaches, this model integrates convolutional layers, an encoder–decoder framework, and multi-head attention mechanisms for the first time in soil moisture prediction. The convolutional layers capture local spatial features, while the encoder–decoder architecture effectively manages temporal dependencies. Additionally, the multi-head attention mechanism enhances the model’s ability to simultaneously focus on multiple key influencing factors, ensuring a comprehensive understanding of complex environmental variables. This synergistic combination significantly improves predictive performance, particularly in challenging climatic conditions. The model was validated using the LandBench1.0 dataset, which includes multiple high-resolution datasets, such as ERA5-land, ERA5 atmospheric variables, and SoilGrids, covering various climatic regions, including high latitudes, temperate zones, and tropical areas. The superior performance of the CAEDLSTM model is evidenced by comparisons with advanced models such as AEDLSTM, CNNLSTM, EDLSTM, and AttLSTM. Relative to the traditional LSTM model, CAEDLSTM achieved an average increase of 5.01% in R2, a 12.89% reduction in RMSE, a 16.67% decrease in bias, and a 4.35% increase in KGE. Moreover, it effectively addresses the limitations of traditional deep learning methods in challenging climates, including tropical Africa, the Tibetan Plateau, and Southeast Asia, resulting in significant enhancements in predictive accuracy within these regions, with R2 values improving by as much as 20%. These results underscore the capabilities of CAEDLSTM in capturing complex soil moisture dynamics, demonstrating its considerable potential for applications in agriculture and water resource monitoring across diverse climates.

Air moisture harvesting in soil for indoor agriculture: A comparative study of moisture dynamics in shallow porous beds

Indoor farming can mitigate water scarcity, declining crop yields, and excessive chemical use in agriculture. However, it demands innovative solutions to reach its full potential. This paper presents a novel indoor plant cultivation technique that leverages atmospheric moisture. Shallow soil bed cooling from below can induce condensation within the soil pores, providing a sustainable water source for plant growth. We tested this method on wheat seed cultivation, observing a 40% growth increase in seedlings with cooled soil beds. We conducted a detailed study of moisture dynamics in porous sand beds to understand the underlying mechanisms of this technique. Choosing sand as a medium isolated the effects of porosity, temperature, and capillary action on moisture condensation. Sand's inertness allows a concentrated analysis of moisture dynamics without interference from chemical reactions or microbial activity. Experiments with cooled sand of varying particle sizes showed moisture condensation levels of 0.025, 0.042, and 0.092 kg/kg for coarse, fine, and superfine sand over 11 days. In soil, moisture reached 0.124 kg/kg over 22 days, highlighting the impact of porosity, temperature, and capillary forces. Our findings reveal exponential moisture increase over time and a linear relationship between bed water content and specific heat. The method is practical and adaptable, especially for remote locations and arid regions, as renewable energy sources can power it. This approach could revolutionize indoor agriculture, particularly in controlled environment systems. Controlling soil temperature can optimize growth conditions, increase yields, and minimize environmental impact. It offers versatility and scalability for various crops and systems.

Sampling frequency significantly influenced surface soil moisture dynamics but not its prediction accuracy in an arid mountain forest

Study regiona typical arid mountain region of northwestern China. Study focusSoil water content (SWC) is the key factor regulating patchy vegetation patterns in arid/semiarid areas. However, accurately determining the regional SWC status remains a challenge due to the time and labor-intensive nature of manual sampling methods. Furthermore, a thorough understanding of the influence of different sampling frequencies (SFs) on SWC spatio-temporal dynamics in arid mountain forests is lacking. New hydrological insights for the regionSFs had a distinct effect on mean SWC, and temporal stability characteristics under lower (15–45 days, LSFs) and higher SFs (within 7 days, HSFs). SF influenced mean SWC for 0–20 cm under HSFs only but had a significant influence for 0–20 and 40–60 cm under LSFs. SF did not influence Spearman’s rank correlation coefficient (rs) for the 0–20 cm layer, but had a significant effect on the standard deviation of mean relative difference (SDRD) under HSFs; however, SF had a significant effect on rs for the deep layer (80–100 cm), but did not influence SDRD under LSFs. Although the number of representative locations (RLs) was significantly higher under HSFs than LSFs, no RLs were found at 100–120 cm. The mean SWC for all soil depths except 40–100 cm under HSFs was predicted accurately for each SF. This indicated that HSFs were not conducive to the identification of deep soil RLs, and had a significant impact on the prediction accuracy of SWC for deep layers. LSFs were not conducive to the identification of surface soil RLs but they can accurately estimate mean SWC, and prediction accuracy improved when SF was reduced. These results have important implications for optimizing water sampling schemes and promoting sustainable ecological development in water-deficient regions.

A review of herbaceous vegetation effect on mechanical properties of soil for enhancing slope stability

Abstract Slope instability is a critical issue that threatens infrastructure, human settlements, and the environment, with conventional stabilization methods often causing significant environmental disruption and incurring high costs. This study aims to explore the potential of native Southeast Asian plants, especially lemongrass (Cymbopogon citratus), vetiver grass (Chrysopogon zizanioides), elephant grass (Pennisetum purpureum), and alang-alang grass (Imperata cylindrica) to improve soil quality and prevent slope stability. The analysis focuses on their physical and mechanical properties to identify trends and advantages for slope reinforcement and sustainable ecological restoration in Southeast Asia. The findings highlight that lemongrass and vetiver grass demonstrate promising improvements in slope stability parameters, including friction angle, cohesion, and shear strength, making them highly effective for erosion control. Lemongrass shows the most pronounced benefits, while vetiver grass also provides substantial stabilization. In contrast, elephant grass and alang-alang grass offer less improvement in these parameters. The study suggests that future research should explore the hydraulic behavior of these species to better understand their effects on water infiltration, runoff, and soil moisture dynamics, which are crucial for comprehensive slope stability analysis.

Influence of bio-aggregates on the physical and hygrothermal properties of bio-concretes

The escalating demand for sustainable and energy-efficient buildings has driven research into innovative construction materials such as bio-concrete. This study assesses the influence of bio-aggregates such as bamboo particles, wood shavings, and rice husk in volume ranging from 40 % to 50 % on bio-concrete's physical and hygrothermal properties. Three bio-concrete types were fabricated: Bamboo Bio-Concrete (BBC), Wood Bio-Concrete (WBC) and Rice Husk Bio-Concrete (RHBC), while maintaining a constant cementitious matrix and varying the bio-aggregate volume. In order to characterize the bio-concretes various analyses and tests were carried out such as scanning electron microscopic (SEM) analyses and Moisture Buffer Value (MBV) tests for bio-aggregates in addition to tests for apparent density, thermal conductivity, capillary water absorption, drying index test, MBV and water vapor permeability (WVP) for the studied bio-concretes. Results reveal that despite diverse microstructural characteristics, all bio-aggregates exhibited MBV values above 3.0 [g/ (m2% RH)], highlighting their role in determining hygrothermal properties. An increase in bio-aggregate volume led to improved hygrothermal properties for bio-concretes (thermal conductivity between 0.19 and 0.25 W/m.K and MBV and WVP in the range of 2.31–3.79 [g/ (m2.% RH)] and 1.11–1.97 × 10−11 [kg/(m.s. Pa)] respectively). Bamboo bio-concrete consistently exhibited higher density values, while rice husk bio-concrete demonstrated superior moisture dynamics and water vapor permeability. Wood bio-concretes exhibit intermediate behavior between bamboo and rice husk bio-concrete. A statistical analysis confirmed these trends. The study underscores bio-concrete's potential for passive hygrothermal regulation in internal building applications, contributing to enhanced energy efficiency and sustainable construction practices.

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.

Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data

Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R2 ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Retreating ice sheet caused a transition from cold-dry to cold-humid conditions in arid Central Asia

Water is critical for ecological systems in arid regions, making it imperative to understand how moisture in arid Central Asia (CA) responds to anthropogenic warming. The oscillation of warming and cooling events since the Last Glacial Maximum (LGM, ∼24–19.5 ka) provides a window for exploring the relationship between moisture and temperature. Employing 109 luminescence ages derived from eight sand dune sediment cores in the Bayanbulak Basin in the CA, this study endeavors to reconstruct the evolution of sand accumulation, and by extension, moisture dynamics. We found that pre-Holocene sand accumulation was predominant during the LGM and Heinrich Stadial 1 (HS1, ∼18–14.6 ka), indicative of a cold-dry climate prevailing during these two cold stages. During the Holocene, sand accumulation during Early Holocene is significantly stronger than that during Middle-late Holocene, supporting a long-term wetting trend. Additionally, this study reveals that the colder Little Ice Age (LIA, ∼0.55–0.2 ka) exhibited a wetter condition compared to the warmer Medieval Warming Period (MWP, ∼1–0.55 ka), indicating a cold-humid climate during the LIA. Corroborated by TraCE-21ka (Transient Climate of the Last 21,000 Years) simulation, we propose that diminished evaporation over North Atlantic during the LGM and HS1 potentially led to a reduction in water vapor transported by westerlies to the CA. During the Middle and Late Holocene, increased evaporation over North Atlantic, attributed to decreased ice sheet, westerlies intensity became the primary limiting factor. Notably, stronger westerlies during the LIA could have contributed to elevated moisture levels compared to the MWP. These findings not only resolve the debate surrounding the transition from cold-dry to cold-humid conditions but also enhance our comprehension of future moisture variations.

An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment

Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-h fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.